Search Results (137)

Air-to-air (A2A) and air-to-ground (A2G) communication links are typical link types for unmanned aerial vehicle (UAV) communication networks, where radio propagation channels are fundamental for the design and optimization of corresponding communication systems. In this paper, a UAV channel measurement system based on two unmanned aerial vehicles (UAVs) is developed, which is capable of simultaneous A2A and A2G measurements. This system adopts an integrated hardware and signal processing architecture that ensures time and frequency synchronization among multiple aerial and ground nodes. Several data postprocessing steps, including the back-to-back calibration, sliding-correlation-based channel impulse response (CIR) extraction, and constant false alarm rate (CFAR)-based multi-path extraction, are performed to achieve accurate channel data. A channel emulator is used to validate the accuracy of the developed system. Finally, the developed channel measurement system is applied to conduct field channel measurements in a campus scenario. Measured channel characteristics, including path loss (PL), shadow fading (SF), Rician K-factor, root mean square delay spread (RMS-DS), and small-scale fading (SSF) are analyzed, which reveal distinct propagation behaviors between the A2A and A2G channels. These results provide valuable experimental insights and channel measurement data for modeling UAV channels. Full article

With the rapid development of mobile edge computing (MEC) and unmanned aerial vehicle (UAV) communication networks, UAV-assisted edge computing has emerged as a promising paradigm for low-latency and energy-efficient computation. However, the time-varying nature of air-to-ground channels and the coupling between UAV trajectories and computation offloading decisions significantly increase system complexity. To address these challenges, this paper proposes an Adaptive UAV Edge Intelligence Framework (AUEIF) for joint UAV computation offloading and trajectory optimization under dynamic channels. Specifically, a dynamic graph-based system model is constructed to characterize the spatio-temporal correlation between UAV motion and channel variations. A hierarchical reinforcement learning-based optimization framework is developed, in which a high-level actor–critic module is responsible for generating coarse-grained UAV flight trajectories, while a low-level deep Q-network performs fine-grained optimization of task offloading ratios and computational resource allocation in real time. In addition, an adaptive channel prediction module leveraging long short-term memory (LSTM) networks is integrated to model temporal channel state transitions and to assist policy learning and updates. Extensive simulation results demonstrate that the proposed AUEIF achieves significant improvements in end-to-end latency, energy efficiency, and overall system stability compared with conventional deep reinforcement learning approaches and heuristic-based schemes while exhibiting strong robustness against dynamic and fluctuating wireless channel conditions. Full article

A series of multi-parameter, multi-layer observations was conducted to study possible electromagnetic precursors associated with the M 6.7 earthquake that struck Iburi, Hokkaido, Japan, at 18:07:59 UT on 5 September 2018. The most significant observation is seismogenic lower-ionospheric perturbations in the propagation anomalies of sub-ionospheric VLF/LF signals recorded in Japan and Russia. Other substantial observations include the GIM-TEC irregularities, the intensification of stratospheric atmospheric gravity waves (AGWs), and the satellite and ground monitoring of air temperature (T), relative humidity (RH), atmospheric chemical potential (ACP), and surface latent heat flux (SLHF). We have found that there were very remarkable VLF/LF anomalies indicative of lower-ionospheric perturbations observed on 4 and 5 September just before the EQ date and even after it from the observations in Japan and Russia. In particular, the anomaly was detected for a particular propagation path from the JJY transmitter (Fukushima) to a VLF station at Wakkanai one day before the EQ, i.e., on 4 September, and is objectively confirmed by machine/deep learning analysis. An anomaly in TEC occurred only on 5 September, but it is unclear whether it is related to a pre-EQ effect or a minor geomagnetic storm. We attempted to determine whether any seismo-related atmospheric gravity wave (AGW) activity occurred in the stratosphere. Although numerous anomalies were detected, they are most likely associated with convective weather phenomena, including a typhoon. Finally, the Earth’s surface parameters based on satellite monitoring seem to indicate some anomalies from 29 August to 3, 4, and 5 September, a few days prior to EQ data, but the ground-based observation close to the EQ epicenter has indicated a clear T/RH and ACP on 2 September with fair weather, but no significant data on subsequent days because of severe meteorological activities. By integrating multi-layer observations, the LAIC (lithosphere–atmosphere–ionosphere coupling) process for the Hokkaido earthquake appears to follow a slow diffusion-type channel, where ionospheric perturbations arise a few days after ground thermal anomalies. This study also provides integrated evidence linking concurrent lower-ionospheric, atmospheric, and surface thermal anomalies, emphasizing the diagnostic value of such multi-parameter observations in understanding EQ-associated precursor signatures. Full article

Homography estimation of infrared and visible light images is a key visual technique that enables drones to perceive their environment and perform autonomous localization in low-altitude environments. Its potential lies in integration with edge computing and 5G technologies, enabling real-time control of drones within air–ground integrated networks. However, research on homography estimation techniques for low-altitude dynamic viewpoints remains scarce. Additionally, images in low-altitude scenarios suffer from issues such as blurring and jitter, presenting new challenges for homography estimation tasks. To address these issues, this paper proposes a light-weight homography estimation method, LFHomo, comprising two components: two anti-blurring feature extractors with non-shared parameters and a lightweight homography estimator, LFHomoE. The anti-blurring feature extractors introduce in-verse residual layers and feature displacement modules to capture sufficient contextual information in blurred regions and to enable lossless and rapid propagation of feature information. In addition, a spatial-reduction-based channel shuffle and spatial joint attention module is designed to suppress redundant features introduced by lossless transmission, allowing efficient extraction and refinement of informative features at low computational cost. The homography estimator LFHomoE adopts a CNN–GNN hybrid architecture to efficiently model geometric relationships between cross-modal features and to achieve fast prediction of homography matrices. Meanwhile, we construct and annotate an unregistered infrared and visible image dataset from drone perspectives for model training and evaluation. Experimental results show that LFHomo maintains great registration accuracy while significantly reducing model size and inference time. Full article

Wireless sensor networks are installed beneath the earth’s surface to track and assess the condition of the below-ground structures. In these systems, buried sensor nodes identify structural anomalies and transmit the sensed information through both soil and air to a sink node located above the ground. In a river-bridge-pillar-monitoring setup, the sensor node located at the pillar’s base sends signals that propagate through soil, water, and air before being received by the sink positioned beneath the bridge. This signal transmission involves transmission through soil, water, and air media. The transmission of signals through soil, water, and air media is yet to be explored through a defined channel model. This study introduces a channel model where the signal traverses through soil, water, and air, and derives an analytical formulation to represent the associated path loss. In addition, experimental validation of the obtained analytical path-loss was conducted using a LoRa setup. It was observed from analytical and experimental results that soil depth and water level individually affect the path loss significantly. This severe attenuation needs to be addressed before the actual deployment of the network. Full article

This paper presents a comprehensive review of integrated terahertz (THz) and free-space optical (FSO) communication systems, focusing on their potential to address the escalating demands for high-capacity, long-distance, and ultra-reliable transmission in future six-generation (6G) and space–air–ground integrated networks (SAGIN). The study systematically examines recent advancements in three critical areas: channel modeling, transmission performance, and integrated system architectures. Specifically, it analyzes THz and FSO channel characteristics, including attenuation mechanisms, turbulence effects, pointing errors, and noise sources, and compares their complementary strengths under diverse atmospheric conditions. Key findings reveal that THz communication achieves transmission rates up to several Tbps over distances of several kilometers but is constrained by molecular absorption and weather-induced attenuation, while FSO offers superior bandwidth-distance products yet suffers from turbulence-induced fading, posing significant reliability challenges. The integration of THz and FSO through adaptive switching strategies (e.g., hard and soft switching) demonstrates enhanced reliability and spectral efficiency, with experimental results showing seamless data rates exceeding Tbps in hybrid systems. However, challenges persist in transceiver hardware integration, algorithmic optimization, and dynamic resource allocation. The review concludes by identifying future research directions, including the development of unified channel models, shared architectures, and intelligent switching algorithms to achieve robust integrated communication infrastructures. Full article

In response to the demand for high-density urban renewal and quality enhancement, the microclimate of street canyon spaces has become a critical factor influencing pedestrian experience and public space vitality. As key nodes connecting above-ground and underground spaces, the entrances and exits of underground commercial streets are particularly sensitive to temperature, humidity, and wind conditions. This study examined a semi-open street canyon adjacent to Entrance No. 11 of the Jianjun Road Underground Commercial Street in Yancheng City as a case study. Through continuous field measurements and numerical simulations using ENVI-met v5.5.1, we conducted a comprehensive analysis. Five monitoring points were established at a height of 1.5 m to simultaneously record the air temperature, relative humidity, wind speed, and thermal radiation images. The results indicate that ventilation acceleration zones form near openings and channel constrictions, whereas leeward sides and corners are prone to stagnant airflow and heat accumulation. During afternoon periods with strong solar radiation and low wind speeds, the predicted mean vote (PMV) values near the entrance increased significantly. The simulation results were in good agreement with the field observations in terms of both the trend and spatial distribution. On the basis of these findings, optimization strategies are proposed including controlling enclosure ratios and local height-to-width ratios, utilizing ventilation corridors and side openings to guide airflow, and incorporating shading devices and low-emissivity materials to improve pedestrian thermal comfort and accessibility. Full article

This paper addresses the issue of existing research that fails adequately capture the spatiotemporal nonstationarity caused by the building of occlusion and flight dynamics in air-to-ground channels from unmanned aerial vehicles (UAVs) in urban scenarios. This study focuses on the angular-altitude correlations of three key metrics: path loss (PL), shadow fading, and the Ricean K-factor. A dynamic path-loss model incorporating the look-down angle is proposed, an exponential decay model for the shadow-fading standard deviation is constructed, and a model for the angle-dependent variation of the Ricean K-factor is established based on line-of-sight probability. Simulations were conducted in two urban-geometry scenarios using WinProp to evaluate the combined effects of flight altitude and elevation angle. The results indicate that path loss decreases and subsequently stabilizes with increasing elevation angle, the shadow-fading standard deviation decreases significantly, and the Ricean K-factor increases with angle and saturates at high angles, in agreement with theoretical predictions. These models are more adaptable to UAV mobility scenarios than traditional fixed exponential models and provide a useful basis for UAV link planning and system optimization in urban environments. Full article

In urban emergency communication scenarios, building obstructions can reduce the performance of base station (BS) communication networks. To address such issues, this paper proposes an air-ground wireless network enabled by an unmanned aerial vehicle (UAV) and assisted by reconfigurable intelligent surfaces (RIS). This system enhances the efficacy of UAV-enabled MISO networks. Treating the UAV as an intelligent agent moving in 3D space, sensing changes in the channel environment, and adopting zero-forcing (ZF) precoding to eliminate interference from ground users. Meanwhile, joint design is performed for UAV movement, RIS phase shifts, and power allocation for users. We propose two deep reinforcement learning (DRL) algorithms, which are termed D3QN-WF and DDQN-WF, respectively. Simulation results indicate that D3QN-WF achieves a 15.9% higher sum rate and 50.1% greater throughput than the DDQN-WF baseline, while also demonstrating significantly faster convergence. Full article

With the widespread adoption of space–air–ground integrated networks (SAGINs) in next-generation wireless communications, intelligent reflecting surfaces (IRSs) have emerged as a key technology for enhancing system performance through passive link reinforcement. This paper addresses the prevalent issue of channel state information (CSI) uncertainty in practical systems by constructing an IRS-assisted multi-hop SAGIN communication model. To capture the performance degradation caused by channel estimation errors, a norm-bounded uncertainty model is introduced. A simulated annealing (SA)-based phase optimization algorithm is proposed to enhance system robustness and improve worst-case communication quality. Simulation results demonstrate that the proposed method significantly outperforms traditional multiple access strategies (SDMA and NOMA) under various user densities and perturbation levels, highlighting its stability and scalability in complex environments. Full article

Millimeter-wave (mmWave) and terahertz (THz) communications are susceptible to frequent link disruptions and severe performance degradation due to high directionality, significant path loss, and sensitivity to blockages. These challenges are particularly acute in highly dynamic and densely populated user environments. The issues present significant obstacles to ensuring reliability and quality of service (QoS) in future space–air–ground integrated networks. To address these challenges, this paper proposes an adaptive transmission control scheme designed for space–air–ground integrated multi-hop networks operating in the mmWave/THz bands. By analyzing the intermittent connectivity inherent in such networks, the proposed scheme incorporates an incremental factor and a backlog indicator into its congestion control mechanism. This allows for the accurate differentiation between packet losses resulting from network congestion and those caused by channel blockages, such as human body occlusion or beam misalignment. Furthermore, the scheme optimizes the initial congestion window during the slow-start phase and dynamically adapts its transmission strategy during the congestion avoidance phase according to the identified cause of packet loss. Simulation results demonstrate that the proposed method effectively mitigates throughput degradation from link blockages, improves data transmission rates in highly dynamic environments, and sustains more stable end-to-end connectivity. Our proposed scheme achieves a 35% higher throughput than TCP Hybla, 40% lower latency than TCP Veno, and maintains 99.2% link utilization under high mobility. Full article

Currently, the primary random access protocol for satellite communications is Irregular Repetition Slotted ALOHA (IRSA). This protocol leverages interference cancellation and burst repetition based on probabilistic distributions, achieving up to 80% channel utilization in practical use. However, it faces three significant issues: (1) low channel utilization with smaller frame sizes; (2) drastic performance degradation under heavy load, where channel utilization can be lower than that of traditional Slotted ALOHA; and (3) even under optimal load and frame sizes, up to 20% of the valuable satellite channel resources are still wasted despite reaching up to 80% channel utilization. In this paper, we propose the Separated Transmission and Control ALOHA (STCA) protocol, which introduces a space–air–ground layered network and separates the access control process from the satellite to an airborne platform, thus preventing collisions in satellite channels. Additionally, the airborne-platform estimates the load to ensure maximum access rates. Simulation results demonstrate that the STCA protocol significantly outperforms the IRSA protocol in terms of channel utilization. Full article

Although product environmental information serves as an effective tool for promoting green consumption which is a critical lever for advancing broader sustainability goals, its varied impacts across product categories (durable goods vs. fast-moving consumer goods) and the underlying mechanisms remain unexplored. Grounded in the theory of consumption values (TCV), this study investigated the heterogeneous effects and mediating pathways of such information through a comparative analysis of representative products: organic milk (fast-moving consumer goods, FMCGs) and energy-efficient air conditioners (durable goods). The results show the following: (1) epistemic value, which exhibits the strongest association with product environmental information, demonstrates significantly different influence patterns between purchases of green durable goods and green FMCGs across both online and offline channels; (2) in the e-commerce context, green FMCG consumption is mainly driven by product environmental information through the mediating effect of conditional value. For green durable goods, product environmental information influences green consumption through multiple pathways including functional value, conditional value, and epistemic value. This study extends the classic theory of consumption values, and the results suggest that differentiated information strategies of emphasizing conditional value for FMCGs and integrating multi-dimensional values for durables can optimize green consumption promotion. Such strategies hold substantial potential to strengthen the green development of the omnichannel retailing sector, reinforcing its contribution to reaching sustainability objectives. Full article

Reconfigurable intelligent surfaces (RISs), recognized as one of the most promising key technologies for sixth-generation (6G) mobile communications, are characterized by their minimal energy expenditure, cost-effectiveness, and straightforward implementation. In this study, we develop a novel communication channel model that integrates RIS-enabled base stations with unmanned ground vehicles. To enhance the system’s adaptability, we implement a fluid antenna system (FAS) at the unmanned ground vehicle (UGV) terminal. This innovative model demonstrates exceptional versatility across various wireless communication scenarios through the strategic adjustment of active ports. The inherent dynamic reconfigurability of the FAS provides superior flexibility and adaptability in air-to-ground communication environments. In the paper, we derive and study key performance characteristics like the autocorrelation function (ACF), validating the model’s effectiveness. The results demonstrate that the RIS-FAS collaborative scheme significantly enhances channel reliability while effectively addressing critical challenges in 6G networks, including signal blockage and spatial constraints in mobile terminals. Full article

Over-the-air federated learning (Air-FL) has emerged as a promising paradigm that integrates communication and learning, which offers significant potential to enhance model training efficiency and optimize communication resource utilization. This paper addresses the challenge of interference management in multi-cell Air-FL systems, focusing on parallel multi-task scenarios where each cell independently executes distinct training tasks. We begin by analyzing the impact of aggregation errors on local model performance within each cell, aiming to minimize the cumulative optimality gap across all cells. To this end, we formulate an optimization framework that jointly optimizes device transmit power and denoising factors. Leveraging the Pareto boundary theory, we design a centralized optimization scheme that characterizes the trade-offs in system performance. Building upon this, we propose a distributed power control optimization scheme based on interference temperature (IT). This approach decomposes the globally coupled problem into locally solvable subproblems, thereby enabling each cell to adjust its transmit power independently using only local channel state information (CSI). To tackle the non-convexity inherent in these subproblems, we first transform them into convex problems and then develop an analytical solution framework grounded in Lagrangian duality theory. Coupled with a dynamic IT update mechanism, our method iteratively approximates the Pareto optimal boundary. The simulation results demonstrate that the proposed scheme outperforms baseline methods in terms of training convergence speed, cross-cell performance balance, and test accuracy. Moreover, it achieves stable convergence within a limited number of iterations, which validates its practicality and effectiveness in multi-task edge intelligence systems. Full article